Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	138,555
2	South Dakota	136,613
3	Rhode Island	136,071
4	Utah	122,321
5	Tennessee	119,509
6	Arizona	117,133
7	Iowa	113,867
8	Oklahoma	112,425
9	Wisconsin	111,687
10	Nebraska	111,654
11	Arkansas	110,406
12	South Carolina	110,178
13	New Jersey	109,407
14	Alabama	106,384
15	Kansas	105,764
16	Indiana	105,155
17	Mississippi	103,737
18	Idaho	103,457
19	Delaware	103,024
20	Illinois	102,593
21	New York	102,031
22	Nevada	100,721
23	Florida	100,370
24	Montana	100,167
25	Georgia	99,611
26	Wyoming	98,947
27	Kentucky	98,462
28	Texas	98,205
29	Minnesota	97,913
30	Louisiana	97,219
31	Massachusetts	97,102
32	Missouri	97,027
33	California	94,006
34	New Mexico	92,934
35	Connecticut	92,296
36	North Carolina	90,586
37	Ohio	89,836
38	Alaska	89,386
39	Michigan	86,830
40	Pennsylvania	86,249
41	Colorado	85,116
42	West Virginia	82,622
43	Virginia	75,358
44	Maryland	71,711
45	New Hampshire	66,859
46	District of Columbia	65,815
47	Washington	50,650
48	Puerto Rico	48,278
49	Maine	42,050
50	Oregon	41,184
51	Vermont	34,941
52	Hawaii	22,254

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	882
2	New Jersey	445
3	Pennsylvania	443
4	Rhode Island	432
5	Maine	421
6	Delaware	406
7	Minnesota	396
8	Colorado	352
9	Puerto Rico	334
10	New York	330
11	Florida	323
12	New Hampshire	319
13	Massachusetts	318
14	Connecticut	315
15	Illinois	291
16	Alaska	264
17	South Dakota	264
18	Maryland	251
19	West Virginia	238
20	North Carolina	229
21	North Dakota	226
22	Vermont	221
23	Tennessee	207
24	District of Columbia	204
25	South Carolina	203
26	Indiana	191
27	Ohio	182
28	Oregon	182
29	Washington	182
30	Nebraska	180
31	Wisconsin	171
32	Virginia	168
33	Kentucky	165
34	Montana	162
35	Iowa	160
36	Nevada	159
37	Georgia	154
38	Utah	150
39	Idaho	143
40	Missouri	134
41	Kansas	128
42	Louisiana	120
43	Texas	113
44	Wyoming	113
45	New Mexico	111
46	Mississippi	100
47	Oklahoma	82
48	Arkansas	81
49	Arizona	78
50	Hawaii	78
51	Alabama	76
52	California	64

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,825
2	New York	2,619
3	Massachusetts	2,531
4	Rhode Island	2,498
5	Mississippi	2,398
6	Arizona	2,356
7	Connecticut	2,242
8	Louisiana	2,211
9	South Dakota	2,206
10	Alabama	2,190
11	Pennsylvania	2,006
12	North Dakota	1,980
13	Indiana	1,962
14	New Mexico	1,909
15	Illinois	1,887
16	Arkansas	1,886
17	Iowa	1,863
18	South Carolina	1,808
19	Georgia	1,799
20	Michigan	1,787
21	Tennessee	1,749
22	Nevada	1,741
23	Texas	1,713
24	Kansas	1,700
25	Oklahoma	1,692
26	Delaware	1,639
27	Ohio	1,624
28	Florida	1,598
29	West Virginia	1,549
30	District of Columbia	1,548
31	California	1,540
32	Missouri	1,484
33	Montana	1,439
34	Kentucky	1,435
35	Maryland	1,410
36	Wisconsin	1,273
37	Minnesota	1,254
38	Virginia	1,235
39	Wyoming	1,214
40	Nebraska	1,205
41	North Carolina	1,183
42	Idaho	1,129
43	Colorado	1,098
44	New Hampshire	931
45	Washington	712
46	Puerto Rico	686
47	Utah	674
48	Oregon	585
49	Maine	567
50	Alaska	434
51	Vermont	387
52	Hawaii	332

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alaska	8
2	Michigan	6
3	Georgia	5
4	New Jersey	5
5	West Virginia	5
6	Kentucky	4
7	Pennsylvania	4
8	Vermont	4
9	California	3
10	Delaware	3
11	District of Columbia	3
12	Florida	3
13	Montana	3
14	New York	3
15	Arizona	2
16	Arkansas	2
17	Idaho	2
18	Illinois	2
19	Indiana	2
20	Iowa	2
21	Kansas	2
22	Maine	2
23	Maryland	2
24	Mississippi	2
25	Nevada	2
26	New Hampshire	2
27	New Mexico	2
28	North Carolina	2
29	North Dakota	2
30	Ohio	2
31	Oklahoma	2
32	Puerto Rico	2
33	Rhode Island	2
34	Texas	2
35	Alabama	1
36	Colorado	1
37	Connecticut	1
38	Louisiana	1
39	Massachusetts	1
40	Minnesota	1
41	Missouri	1
42	Oregon	1
43	South Carolina	1
44	South Dakota	1
45	Tennessee	1
46	Virginia	1
47	Washington	1
48	Wisconsin	1
49	Hawaii	0
50	Nebraska	0
51	Utah	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	355,387	1	99
Chattahoochee	Georgia	345,925	2	99
Bent	Colorado	266,631	3	99
Dewey	South Dakota	245,927	4	99
Lincoln	Arkansas	245,009	5	99
Davidson	Tennessee	138,899	195	93
Richland	South Carolina	108,351	1014	67
York	South Carolina	105,624	1131	64
Orange	California	84,631	2115	32
Pierce	Washington	50,463	2891	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,539	1779	43
Davidson	Tennessee	1,330	2030	35
York	South Carolina	1,313	2053	34
Richland	South Carolina	1,292	2080	33
Pierce	Washington	696	2719	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.